Generalized Multiview Analysis: A discriminative latent space

This paper presents a general multi-view feature extraction approach that we call Generalized Multiview Analysis or GMA. GMA has all the desirable properties required for cross-view classification and retrieval: it is supervised, it allows generalization to unseen classes, it is multi-view and kernelizable, it affords an efficient eigenvalue based solution and is applicable to any domain. GMA exploits the fact that most popular supervised and unsupervised feature extraction techniques are the solution of a special form of a quadratic constrained quadratic program (QCQP), which can be solved efficiently as a generalized eigenvalue problem. GMA solves a joint, relaxed QCQP over different feature spaces to obtain a single (non)linear subspace. Intuitively, GMA is a supervised extension of Canonical Correlational Analysis (CCA), which is useful for cross-view classification and retrieval. The proposed approach is general and has the potential to replace CCA whenever classification or...

• Type: Journal
• Year: 2012
• Where: CVPR
• Authors: Abhishek Sharma, Abhishek Kumar, Hal Daumé III, David W. Jacobs
• Keywords: Computer Vision, Cvpr 2012, Generalized Eigenvalue Problem, Linear Subspace, Unsupervised Feature Extraction